As an Applied AI/ML Analyst within our dynamic team of innovators and technologists, you will revolutionize how the Bank services and advises clients, deepen client engagements, and promote process transformation. You will analyze existing processes and vast amounts of data to design autonomous AI agents. We seek individuals passionate about leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery. Our culture thrives on experimentation, continuous improvement, and learning. You will work in a collaborative, trusting, and intellectually stimulating environment—one that values diversity of thought and fosters creative solutions that serve the best interests of our global clientele.
**Job Responsibilities**
- Build and maintain end-to-end batch and streaming data pipelines (ingestion → transformation → curation → serving) on AWS.
- Develop scalable transformations using Spark (PySpark/Scala) and SQL, optimizing for performance, reliability, and cost. Implement and optimize Snowflake solutions (schemas, tables, views, micro-partitioning/clustering considerations, warehouse sizing, query tuning, data sharing patterns where applicable).
- Understanding of data models for enterprise analytics (dimensional/star schemas, normalized models; applying standard banking data concepts where relevant). Create robust orchestration and scheduling (e.g., Airflow/MWAA, AWS Step Functions, etc.) with monitoring, alerting, retries, and operational runbooks.
- Ensure data quality through validation checks, reconciliation, and controls (e.g., completeness, timeliness, accuracy, deduplication). Implement security and governance controls suitable for banking: encryption, least-privilege access, auditability, data retention, and lineage/documentation.
- Collaborate with stakeholders across technology, risk, compliance, analytics, and product to translate requirements into reusable, well-defined datasets and support production operations: incident triage, root-cause analysis, performance tuning, and continuous improvement of pipeline stability.
- Collaborate with data science and ML teams to design, build, and maintain scalable data infrastructure that supports the training, deployment, and monitoring of AI/ML models. Continuously evaluate and adopt emerging AI technologies to optimize ETL workflows, reduce manual effort, and enhance overall engineering productivity.
Required qualifications, capabilities, and skills
- Bachelors degree in a quantitative or technical discipline or significant practical experience in industry.
- Strong SQL skills (complex joins, window functions, query optimization, performance troubleshooting) and experience with AWS data services and cloud-native patterns (e.g., S3, IAM, KMS, Glue, Lambda, EMR, Step Functions, Kinesis/MSK—relevant mix).
- Solid understanding of data modeling and how model choices impact performance, usability, and governance.
- Practical experience with Snowflake (data loading, transformations, optimization, access controls). Proficiency in Python (or equivalent) for pipeline development, automation, and testing.
- Experience with Git and CI/CD practices; familiarity with engineering standards for code reviews and automated testing.
Preferred qualifications, capabilities, and skills
- Prior experience of developing solutions for Financial domain
- Exposure to distributed model training, and deployment
- Familiarity with techniques for model explainability and self-validation
- Hands-on development experience with Spark (PySpark preferred) for large-scale processing.